Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	32,424
2	Florida	29,492
3	Mississippi	28,347
4	Arizona	27,875
5	Alabama	26,154
6	Georgia	24,345
7	South Carolina	23,404
8	Nevada	22,702
9	Tennessee	22,672
10	New York	22,666
11	Texas	22,411
12	New Jersey	21,884
13	Iowa	21,065
14	Rhode Island	20,840
15	Arkansas	20,581
16	District of Columbia	19,946
17	Illinois	19,040
18	Idaho	18,499
19	California	18,273
20	Maryland	18,163
21	Nebraska	18,090
22	Delaware	18,021
23	Massachusetts	17,574
24	Utah	16,516
25	North Carolina	16,317
26	North Dakota	16,102
27	South Dakota	15,828
28	Kansas	15,284
29	Oklahoma	15,192
30	Connecticut	14,895
31	Indiana	14,535
32	Missouri	14,479
33	Virginia	14,356
34	Wisconsin	14,128
35	Minnesota	13,677
36	New Mexico	12,214
37	Kentucky	11,796
38	Michigan	11,475
39	Pennsylvania	10,980
40	Ohio	10,759
41	Puerto Rico	10,646
42	Washington	10,315
43	Colorado	10,139
44	Alaska	8,449
45	Montana	7,218
46	Wyoming	6,757
47	Oregon	6,421
48	Hawaii	6,332
49	West Virginia	5,938
50	New Hampshire	5,375
51	Maine	3,397
52	Vermont	2,623

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Kansas	288
2	South Dakota	256
3	North Dakota	247
4	Tennessee	223
5	Missouri	206
6	Alabama	204
7	Iowa	196
8	Louisiana	193
9	Idaho	191
10	Mississippi	189
11	Kentucky	188
12	Florida	184
13	Georgia	177
14	Oklahoma	176
15	Nebraska	163
16	Hawaii	153
17	Texas	152
18	North Carolina	147
19	Illinois	144
20	South Carolina	141
21	Arkansas	138
22	California	130
23	Rhode Island	124
24	Indiana	121
25	Puerto Rico	120
26	Montana	116
27	Utah	113
28	Minnesota	112
29	Wisconsin	111
30	Virginia	109
31	Nevada	102
32	Ohio	99
33	West Virginia	98
34	Maryland	83
35	Delaware	70
36	Alaska	66
37	Michigan	66
38	Pennsylvania	63
39	Washington	62
40	Connecticut	57
41	Arizona	55
42	District of Columbia	55
43	New Mexico	52
44	Wyoming	52
45	Colorado	50
46	Oregon	41
47	New Jersey	37
48	New York	36
49	Maine	13
50	New Hampshire	13
51	Vermont	11
52	Massachusetts	-343

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,797
2	New York	1,673
3	Massachusetts	1,314
4	Connecticut	1,252
5	Louisiana	1,076
6	Rhode Island	992
7	District of Columbia	861
8	Mississippi	848
9	Arizona	696
10	Michigan	679
11	Illinois	656
12	Delaware	622
13	Maryland	622
14	Pennsylvania	608
15	South Carolina	542
16	Florida	535
17	Georgia	533
18	Indiana	493
19	Texas	454
20	Alabama	452
21	Nevada	434
22	New Mexico	376
23	Iowa	359
24	Ohio	357
25	Colorado	340
26	California	337
27	Minnesota	333
28	New Hampshire	317
29	Virginia	309
30	Arkansas	278
31	Missouri	271
32	North Carolina	267
33	Washington	266
34	Tennessee	258
35	Kentucky	225
36	Nebraska	210
37	Idaho	209
38	Oklahoma	207
39	North Dakota	199
40	Wisconsin	197
41	South Dakota	191
42	Kansas	157
43	Puerto Rico	138
44	Utah	129
45	West Virginia	128
46	Oregon	112
47	Montana	101
48	Maine	98
49	Vermont	92
50	Wyoming	70
51	Hawaii	52
52	Alaska	49

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	9
2	Arkansas	6
3	Florida	5
4	Georgia	5
5	Louisiana	5
6	South Carolina	5
7	Texas	4
8	Alabama	3
9	California	3
10	Kentucky	3
11	Nevada	3
12	New Mexico	3
13	West Virginia	3
14	Hawaii	2
15	Idaho	2
16	Iowa	2
17	Missouri	2
18	North Carolina	2
19	North Dakota	2
20	Tennessee	2
21	Virginia	2
22	Wyoming	2
23	Arizona	1
24	Illinois	1
25	Indiana	1
26	Michigan	1
27	Minnesota	1
28	Montana	1
29	Nebraska	1
30	New Jersey	1
31	Ohio	1
32	Oklahoma	1
33	Pennsylvania	1
34	Rhode Island	1
35	Washington	1
36	Wisconsin	1
37	Alaska	0
38	Colorado	0
39	Connecticut	0
40	Delaware	0
41	District of Columbia	0
42	Kansas	0
43	Maine	0
44	Maryland	0
45	Massachusetts	0
46	New Hampshire	0
47	New York	0
48	Oregon	0
49	Puerto Rico	0
50	South Dakota	0
51	Utah	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	143,477	1	99
Lafayette	Florida	143,078	2	99
Lincoln	Arkansas	123,618	3	99
Lake	Tennessee	120,867	4	99
Chattahoochee	Georgia	117,081	5	99
Davidson	Tennessee	37,690	164	94
Richland	South Carolina	27,516	360	88
Orange	California	15,474	1022	67
York	South Carolina	15,446	1025	67
Pierce	Washington	8,396	1869	40

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,612	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Neshoba	Mississippi	3,469	5	99
Richland	South Carolina	471	696	77
Davidson	Tennessee	375	898	71
Orange	California	317	1045	66
Pierce	Washington	210	1400	55
York	South Carolina	174	1548	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.